本研究旨在檢視由 Baba-Yara, Boons, and Tamoni (2024) 所提出之特徵分解框架，是否適用於以散戶為主導的台灣股票市場。本研究採用 1989 年至 2024 年之月分數據，透過雙重排序程序（Double-sorting procedure），將六大核心公司特徵——帳面市值比、資產報酬率、資產成長、動能、短期反轉與公司規模——成功分解為持續性與暫時性成分。實證結果顯示，不同公司特徵之間的報酬特性存在顯著的異質性。資產報酬率與短期反轉主要由暫時性成分所主導，其報酬價差呈現迅速衰減的趨勢。與美國市場的實證證據相反，動能與公司規模在台灣市場展現出強烈的持續性成分，其條件 Alpha即使在長期排序期間下依然保持顯著。此外，帳面市值比與公司規模的主導地位會依據排序期間的不同，在持續性與暫時性成分之間動態位移。上述實證分歧與台灣股市以散戶為主體且受行為驅動的市場結構吻合。投資組合分析進一步表明，透過最佳線性組合將這兩種成分相結合，能顯著提升動能因子的夏普值，並在中短期排序期間時達到的最大效益改善；然而，基於規模的特徵分解所帶來的投資增益則相對有限。整體而言，研究結果支持特徵分解框架在新興市場脈絡下，能作為投資人與資產管理經理人區分風險補償與市場錯價的一項實用工具。

This study examines the time-series structure of characteristic-contributed returns in the retail-dominated Taiwan stock market from 1989 to 2024. We decompose six core firm characteristics into persistent and transitory components to separate long-term risk compensation from short-term mispricing.
The empirical results indicate that the six characteristics exhibit substantial heterogeneity in their time variation and distinct return decay dynamics. Profitability (return on assets) displays powerful immediate predictive power, which decays rapidly over subsequent short horizons. Furthermore, the results reveal a dynamic competition between the decay rate of excess returns and the decay rate of risk exposures within the profitability and value (book-to-market) characteristics. This shows that the two components undergo structural fluctuations over time, reflecting that the underlying returns are jointly driven by transient market mispricing and long-term risk premiums. Notably, regarding the size (SIZE) characteristic, this study finds that the size effect in Taiwan is predominantly driven by a persistent component. However, a critical divergence emerges regarding their predictive horizons. In mature markets, size operates as a conventional long-term risk compensation with extended, long-term predictive power. Conversely, our empirical evidence reveals that the predictive power of the size effect in Taiwan is highly concentrated within the short-to-medium term, becoming statistically insignificant over longer forecasting horizons.
From a portfolio perspective, mean-variance optimization shows that filtering out short-term noise through this framework significantly improves risk-adjusted performance. These portfolio gains are due to the structural directional allocation of components rather than to a simple hedging strategy. Collectively, these findings validate the characteristic decomposition framework and offer valuable guidance for multi-factor quantitative investments in emerging markets.

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